MicroCloud Hologram Inc. Develops Quantum Algorithm Technology for Deep Convolutional Neural Network Exchange Submissions

SHENZHEN, China, Jan. 21, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, have developed a quantum algorithm technology for deep convolutional neural network (CNN) exchange submissions, aimed at overcoming the computational bottlenecks of traditional CNNs and achieving performance improvements through the advantages of quantum computing. The core innovation of this technology lies in the design and implementation of the Quantum Convolutional Neural Network (QCNN). The QCNN not only fully replicates the output of classical CNNs but also overcomes common challenges in quantum computing, such as the difficulty of implementing nonlinear operations. Through carefully designed quantum circuits, HOLO has successfully implemented nonlinear activation functions and pooling operations within the quantum framework, opening a new door for quantum deep learning. More importantly, the architecture of the QCNN significantly improves computational efficiency in both forward and backward propagation, providing strong support for the training process of deep neural networks.

From a technical perspective, the implementation of QCNN consists of several key modules. First, it designs an input method based on quantum state encoding, which maps high-dimensional data into quantum states. This encoding technique leverages the properties of quantum state superposition and entanglement, allowing the convolution operation to be performed in parallel within high-dimensional space, significantly reducing computational complexity. Next, HOLO developed a set of quantum convolution kernels, which are implemented as unitary operations and can efficiently extract local features from the input data. By combining the inner product calculations of quantum states, the convolution process is completed at quantum speed.

For the implementation of nonlinear activation functions, HOLO introduces a measurement-based nonlinear operation. By performing partial measurements on quantum states, this approach achieves nonlinear mapping while preserving quantum superposition. This method overcomes the bottleneck of implementing nonlinear operations in quantum computing, while maintaining the unitarity of the computational process. Furthermore, QCNN also supports pooling operations, which are performed through reduction measurements of quantum states, making the feature dimension reduction process more efficient.

In terms of training, HOLO proposes an optimization algorithm based on quantum gradient computation. This method utilizes the parameterized representation of quantum states and combines it with the gradient descent method, enabling efficient updates of network parameters. To validate the performance of QCNN, numerical simulations of classification tasks were conducted on relevant datasets. The results show that, compared to classical CNNs, QCNN achieves comparable classification accuracy, but with significant advantages in computational speed and resource utilization efficiency. Particularly when handling large-scale datasets and high-dimensional inputs, the potential of QCNN is fully demonstrated.

The development of this technology is not only theoretically groundbreaking but also shows great potential in practical applications. In the field of image recognition, the performance improvements of QCNN enable it to handle more complex tasks in various scenarios. For instance, in medical image analysis, QCNN can quickly and accurately detect abnormal lesions, providing doctors with reliable diagnostic support. In the autonomous driving domain, QCNN's efficient computational capabilities allow real-time processing of environmental information around the vehicle, enhancing driving safety. Furthermore, QCNN also holds potential value in fields such as natural language processing and financial data analysis.

Although HOLO's QCNN has made significant progress, future research directions remain full of challenges and opportunities. First, further optimizing quantum circuits to handle larger datasets and more complex tasks is an issue worth exploring. Additionally, limitations in quantum computing hardware, such as noise and the constraints on the number of qubits, remain major bottlenecks for the technology's development. To address these issues, it is essential to continue exploring more robust quantum algorithm designs while closely monitoring developments in quantum hardware to ensure the practical feasibility of the technology.

Quantum Convolutional Neural Networks (QCNN), as an innovative deep learning framework, not only provide new ideas for the practical application of quantum computing but also bring infinite possibilities for the future development of deep learning. The implementation of HOLO's quantum algorithm technology for deep convolutional neural network exchange submissions not only demonstrates the immense potential of combining quantum computing with machine learning but also marks an important step toward a new era of intelligent computing.

Looking ahead, the potential of quantum convolutional neural networks will continue to be explored with the further advancements in quantum computing hardware. The breakthrough significance of this technology lies not only in its ability to address current computational bottlenecks but also in the new perspective it brings to the field of deep learning. The parallelism and superposition capabilities of quantum computing enable QCNN to efficiently process high-dimensional data, showing exceptional adaptability, especially when faced with increasingly complex data environments. By deeply integrating with industry needs, QCNN is expected to play an irreplaceable role in fields such as healthcare, transportation, finance, and fundamental science.

More importantly, the success of this technology also lays the foundation for the development of next-generation intelligent systems. From quantum artificial intelligence to collaborative frameworks for distributed quantum computing, the development of QCNN marks our progression toward a new computing era driven by quantum technology. This is not just a technological leap, but also a significant driving force for socioeconomic development. The power of quantum computing will provide entirely new solutions to many complex problems humanity faces. The successful development of QCNN is the starting point of this journey and is destined to become a milestone in the future integration of quantum technology and artificial intelligence.

About MicroCloud Hologram Inc.

MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud's holographic technology services include high-precision holographic light detection and ranging ("LiDAR") solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems ("ADAS"). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud's holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud's holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit http://ir.mcholo.com/

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SOURCE MicroCloud Hologram Inc.